LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping

High-resolution phenotyping at the level of individual leaves offers fine-grained insights into plant development and stress responses. However, the full potential of accurate leaf tracking over time remains largely unexplored due to the absence of robust tracking methods, particularly for structurally complex crops such as canola. Existing plant-specific tracking methods are typically limited to small-scale species or rely on constrained imaging conditions. In contrast, generic multi-object tracking (MOT) methods are not designed for dynamic biological scenes. Progress in the development of accurate leaf tracking models has also been hindered by a lack of large-scale datasets captured under realistic conditions. In this work, we introduce CanolaTrack, a new benchmark dataset comprising 5704 RGB images with 31,840 annotated leaf instances collected from 184 canola plants during their early growth stages. To enable accurate leaf tracking over time, we introduce LeafTrackNet, an efficient framework that combines a YOLOv10-based leaf detector with a MobileNetV3-based embedding network. During inference, leaf identities are maintained over time through an embedding-based memory association strategy. When trained directly on each target dataset without prior CanolaTrack fine-tuning, LeafTrackNet achieves HOTA scores of 88.03, 87.33, and 74.20 on CanolaTrack, KOMATSUNA, and MSU-PID, respectively, outperforming the corresponding second-best methods by 8.35, 4.94, and 1.62 HOTA points. This work provides a new benchmark for leaf-level tracking under realistic conditions and introduces CanolaTrack, which, to the best of our knowledge, is the largest leaf-tracking dataset for agricultural crops. Our code and dataset are publicly available at GitHub.